def test_linked_double_linked1():
'''
Linked to a linked variable, without indices
'''
G1 = NeuronGroup(10, 'x : 1')
G2 = NeuronGroup(10, 'y : 1 (linked)')
G2.y = linked_var(G1.x)
G3 = NeuronGroup(10, 'z: 1 (linked)')
G3.z = linked_var(G2.y)
G1.x = np.arange(10)
assert_equal(G3.z[:], np.arange(10))
def test_linked_double_linked4():
'''
Linked to a linked variable, both use indices
'''
G1 = NeuronGroup(5, 'x : 1')
G2 = NeuronGroup(10, 'y : 1 (linked)')
G2.y = linked_var(G1.x, index=np.arange(5).repeat(2))
G3 = NeuronGroup(10, 'z: 1 (linked)')
G3.z = linked_var(G2.y, index=np.arange(10)[::-1])
G1.x = np.arange(5)*0.1
assert_equal(G3.z[:], np.arange(5).repeat(2)[::-1]*0.1)
def test_linked_double_linked3():
'''
Linked to a linked variable, first with indices, second without indices
'''
G1 = NeuronGroup(5, 'x : 1')
G2 = NeuronGroup(10, 'y : 1 (linked)')
G2.y = linked_var(G1.x, index=np.arange(5).repeat(2))
G3 = NeuronGroup(10, 'z: 1 (linked)')
G3.z = linked_var(G2.y)
G1.x = np.arange(5)*0.1
assert_equal(G3.z[:], np.arange(5).repeat(2)*0.1)
def test_linked_triple_linked():
'''
Link to a linked variable that links to a linked variable, all use indices
'''
G1 = NeuronGroup(2, 'a : 1')
G2 = NeuronGroup(4, 'b : 1 (linked)')
G2.b = linked_var(G1.a, index=np.arange(2).repeat(2))
G3 = NeuronGroup(4, 'c: 1 (linked)')
G3.c = linked_var(G2.b, index=np.arange(4)[::-1])
G4 = NeuronGroup(8, 'd: 1 (linked)')
G4.d = linked_var(G3.c, index=np.arange(4).repeat(2))
G1.a = np.arange(2)*0.1
assert_equal(G4.d[:], np.arange(2).repeat(2)[::-1].repeat(2)*0.1)
def test_linked_synapses():
'''
Test linking to a synaptic variable (should raise an error).
'''
G = NeuronGroup(10, '')
S = Synapses(G, G, 'w:1', connect=True)
G2 = NeuronGroup(100, 'x : 1 (linked)')
assert_raises(NotImplementedError, lambda: setattr(G2, 'x', linked_var(S, 'w')))
def test_linked_variable_repeat():
'''
Test a "repeat"-like connection between two groups of different size
'''
G1 = NeuronGroup(5, 'w : 1')
G2 = NeuronGroup(10, 'v : 1 (linked)')
G2.v = linked_var(G1.w, index=np.arange(5).repeat(2))
G1.w = np.arange(5) * 0.1
assert_equal(G2.v[:], np.arange(5).repeat(2) * 0.1)
def test_linked_variable_incorrect():
'''
Test incorrect uses of linked variables.
'''
G1 = NeuronGroup(10, '''x : volt
y : 1''')
G2 = NeuronGroup(20, '''x: volt''')
G3 = NeuronGroup(10, '''l : volt (linked)
not_linked : volt''')
# incorrect unit
assert_raises(DimensionMismatchError, lambda: setattr(G3, 'l', linked_var(G1.y)))
# incorrect group size
assert_raises(ValueError, lambda: setattr(G3, 'l', linked_var(G2.x)))
# incorrect use of linked_var
assert_raises(ValueError, lambda: setattr(G3, 'l', linked_var(G1.x, 'x')))
assert_raises(ValueError, lambda: setattr(G3, 'l', linked_var(G1)))
# Not a linked variable
assert_raises(TypeError, lambda: setattr(G3, 'not_linked', linked_var(G1.x)))
def test_linked_variable_indexed():
'''
Test linking a variable with an index specified as an array
'''
G = NeuronGroup(10, '''x : 1
y : 1 (linked)''')
G.x = np.arange(10)*0.1
G.y = linked_var(G.x, index=np.arange(10)[::-1])
# G.y should refer to an inverted version of G.x
assert_equal(G.y[:], np.arange(10)[::-1]*0.1)
def test_linked_var_in_reset_size_1():
G1 = NeuronGroup(1, 'x:1')
G2 = NeuronGroup(1, '''x_linked : 1 (linked)
y:1''',
threshold='y>1', reset='y=0; x_linked += 1')
G2.x_linked = linked_var(G1, 'x')
G2.y = 1.1
# In this context, x_linked should not be considered as a scalar variable
# and therefore the reset statement should be allowed
run(3*defaultclock.dt)
assert_equal(G1.x[:], 1)
def test_linked_subgroup():
'''
Test linking a variable from a subgroup
'''
G1 = NeuronGroup(10, 'x : 1')
G1.x = np.arange(10) * 0.1
G2 = G1[3:8]
G3 = NeuronGroup(5, 'y:1 (linked)')
G3.y = linked_var(G2.x)
assert_equal(G3.y[:], (np.arange(5)+3)*0.1)
def test_linked_subgroup2():
'''
Test linking a variable from a subgroup with indexing
'''
G1 = NeuronGroup(10, 'x : 1')
G1.x = np.arange(10) * 0.1
G2 = G1[3:8]
G3 = NeuronGroup(10, 'y:1 (linked)')
G3.y = linked_var(G2.x, index=np.arange(5).repeat(2))
assert_equal(G3.y[:], (np.arange(5)+3).repeat(2)*0.1)
def test_linked_var_in_reset_size_1():
G1 = NeuronGroup(1, 'x:1')
G2 = NeuronGroup(1, '''x_linked : 1 (linked)
y:1''',
threshold='y>1', reset='y=0; x_linked += 1')
G2.x_linked = linked_var(G1, 'x')
G2.y = 1.1
# In this context, x_linked should not be considered as a scalar variable
# and therefore the reset statement should be allowed
run(3*defaultclock.dt)
assert_equal(G1.x[:], 1)
def test_linked_var_in_reset_incorrect():
# Raise an error if a scalar variable (linked variable from a group of size
# 1 is set in a reset statement of a group with size > 1)
G1 = NeuronGroup(1, 'x:1')
G2 = NeuronGroup(2, '''x_linked : 1 (linked)
y:1''',
threshold='y>1', reset='y=0; x_linked += 1')
G2.x_linked = linked_var(G1, 'x')
G2.y = 1.1
net = Network(G1, G2)
# It is not well-defined what x_linked +=1 means in this context
# (as for any other shared variable)
assert_raises(SyntaxError, lambda: net.run(0*ms))
def test_linked_var_in_reset_incorrect():
# Raise an error if a scalar variable (linked variable from a group of size
# 1 is set in a reset statement of a group with size > 1)
G1 = NeuronGroup(1, 'x:1')
G2 = NeuronGroup(2, '''x_linked : 1 (linked)
y:1''',
threshold='y>1', reset='y=0; x_linked += 1')
G2.x_linked = linked_var(G1, 'x')
G2.y = 1.1
net = Network(G1, G2)
# It is not well-defined what x_linked +=1 means in this context
# (as for any other shared variable)
assert_raises(SyntaxError, lambda: net.run(0*ms))
def test_linked_variable_scalar():
'''
Test linked variable from a size 1 group.
'''
G1 = NeuronGroup(1, 'dx/dt = -x / (10*ms) : 1')
G2 = NeuronGroup(10, '''dy/dt = (-y + x) / (20*ms) : 1
x : 1 (linked)''')
G1.x = 1
G2.y = np.linspace(0, 1, 10)
G2.x = linked_var(G1.x)
mon = StateMonitor(G2, 'y', record=True)
net = Network(G1, G2, mon)
net.run(10*ms)
def test_linked_variable_indexed_incorrect():
'''
Test errors when providing incorrect index arrays
'''
G = NeuronGroup(10, '''x : 1
y : 1 (linked)''')
G.x = np.arange(10) * 0.1
assert_raises(
TypeError,
lambda: setattr(G, 'y', linked_var(G.x, index=np.arange(10) * 1.0)))
assert_raises(
TypeError, lambda: setattr(
G, 'y', linked_var(G.x, index=np.arange(10).reshape(5, 2))))
assert_raises(TypeError,
lambda: setattr(G, 'y', linked_var(G.x, index=np.arange(5))))
assert_raises(
ValueError,
lambda: setattr(G, 'y', linked_var(G.x, index=np.arange(10) - 1)))
assert_raises(
ValueError,
lambda: setattr(G, 'y', linked_var(G.x, index=np.arange(10) + 1)))
def test_linked_variable_indexed_incorrect():
'''
Test errors when providing incorrect index arrays
'''
G = NeuronGroup(10, '''x : 1
y : 1 (linked)''')
G.x = np.arange(10)*0.1
assert_raises(TypeError,
lambda: setattr(G, 'y',
linked_var(G.x, index=np.arange(10)*1.0)))
assert_raises(TypeError,
lambda: setattr(G, 'y',
linked_var(G.x, index=np.arange(10).reshape(5, 2))))
assert_raises(TypeError,
lambda: setattr(G, 'y',
linked_var(G.x, index=np.arange(5))))
assert_raises(ValueError,
lambda: setattr(G, 'y',
linked_var(G.x, index=np.arange(10)-1)))
assert_raises(ValueError,
lambda: setattr(G, 'y',
linked_var(G.x, index=np.arange(10)+1)))
def test_linked_variable_correct():
'''
Test correct uses of linked variables.
'''
tau = 10*ms
G1 = NeuronGroup(10, 'dv/dt = -v / tau : volt')
G1.v = np.linspace(0*mV, 20*mV, 10)
G2 = NeuronGroup(10, 'v : volt (linked)')
G2.v = linked_var(G1.v)
mon1 = StateMonitor(G1, 'v', record=True)
mon2 = StateMonitor(G2, 'v', record=True)
net = Network(G1, G2, mon1, mon2)
net.run(10*ms)
assert_equal(mon1.v[:, :], mon2.v[:, :])
def test_linked_subexpression_2():
'''
Test a linked variable referring to a subexpression without indices
'''
G = NeuronGroup(2, '''dv/dt = 100*Hz : 1
I = clip(v, 0, inf) : 1''',
threshold='v>1', reset='v=0')
G.v = [0, .5]
G2 = NeuronGroup(2, '''I_l : 1 (linked) ''')
G2.I_l = linked_var(G.I)
mon1 = StateMonitor(G, 'I', record=True)
mon = StateMonitor(G2, 'I_l', record=True)
run(5*ms)
assert all(mon[0].I_l == mon1[0].I)
assert all(mon[1].I_l == mon1[1].I)
def test_linked_subexpression_2():
'''
Test a linked variable referring to a subexpression without indices
'''
G = NeuronGroup(2, '''dv/dt = 100*Hz : 1
I = clip(v, 0, inf) : 1''',
threshold='v>1', reset='v=0')
G.v = [0, .5]
G2 = NeuronGroup(2, '''I_l : 1 (linked) ''')
G2.I_l = linked_var(G.I)
mon1 = StateMonitor(G, 'I', record=True)
mon = StateMonitor(G2, 'I_l', record=True)
run(5*ms)
assert all(mon[0].I_l == mon1[0].I)
assert all(mon[1].I_l == mon1[1].I)
def test_linked_subexpression():
'''
Test a subexpression referring to a linked variable.
'''
G = NeuronGroup(2, 'dv/dt = 100*Hz : 1',
threshold='v>1', reset='v=0')
G.v = [0, .5]
G2 = NeuronGroup(10, '''I = clip(x, 0, inf) : 1
x : 1 (linked) ''')
G2.x = linked_var(G.v, index=np.array([0, 1]).repeat(5))
mon = StateMonitor(G2, 'I', record=True)
run(5*ms)
# Due to the linking, the first 5 and the second 5 recorded I vectors should
# be identical
assert all((all(mon[i].I == mon[0].I) for i in xrange(5)))
assert all((all(mon[i+5].I == mon[5].I) for i in xrange(5)))
def test_linked_variable_correct():
'''
Test correct uses of linked variables.
'''
tau = 10*ms
G1 = NeuronGroup(10, 'dv/dt = -v / tau : volt')
G1.v = np.linspace(0*mV, 20*mV, 10)
G2 = NeuronGroup(10, 'v : volt (linked)')
G2.v = linked_var(G1.v)
mon1 = StateMonitor(G1, 'v', record=True)
mon2 = StateMonitor(G2, 'v', record=True)
run(10*ms)
assert_equal(mon1.v[:, :], mon2.v[:, :])
# Make sure that printing the variable values works
assert len(str(G2.v)) > 0
assert len(repr(G2.v)) > 0
assert len(str(G2.v[:])) > 0
assert len(repr(G2.v[:])) > 0
def test_linked_subexpression():
'''
Test a subexpression referring to a linked variable.
'''
G = NeuronGroup(2, 'dv/dt = 100*Hz : 1',
threshold='v>1', reset='v=0')
G.v = [0, .5]
G2 = NeuronGroup(10, '''I = clip(x, 0, inf) : 1
x : 1 (linked) ''')
G2.x = linked_var(G.v, index=np.array([0, 1]).repeat(5))
mon = StateMonitor(G2, 'I', record=True)
run(5*ms)
# Due to the linking, the first 5 and the second 5 recorded I vectors should
# be identical
assert all((all(mon[i].I == mon[0].I) for i in xrange(5)))
assert all((all(mon[i+5].I == mon[5].I) for i in xrange(5)))
def test_linked_variable_correct():
'''
Test correct uses of linked variables.
'''
tau = 10*ms
G1 = NeuronGroup(10, 'dv/dt = -v / tau : volt')
G1.v = linspace(0*mV, 20*mV, 10)
G2 = NeuronGroup(10, 'v : volt (linked)')
G2.v = linked_var(G1.v)
mon1 = StateMonitor(G1, 'v', record=True)
mon2 = StateMonitor(G2, 'v', record=True)
run(10*ms)
assert_equal(mon1.v[:, :], mon2.v[:, :])
# Make sure that printing the variable values works
assert len(str(G2.v)) > 0
assert len(repr(G2.v)) > 0
assert len(str(G2.v[:])) > 0
assert len(repr(G2.v[:])) > 0
def test_linked_variable_scalar():
'''
Test linked variable from a size 1 group.
'''
G1 = NeuronGroup(1, 'dx/dt = -x / (10*ms) : 1')
G2 = NeuronGroup(10, '''dy/dt = (-y + x) / (20*ms) : 1
x : 1 (linked)''')
G1.x = 1
G2.y = np.linspace(0, 1, 10)
G2.x = linked_var(G1.x)
mon = StateMonitor(G2, 'y', record=True)
# We don't test anything for now, except that it runs without raising an
# error
run(10*ms)
# Make sure that printing the variable values works
assert len(str(G2.x)) > 0
assert len(repr(G2.x)) > 0
assert len(str(G2.x[:])) > 0
assert len(repr(G2.x[:])) > 0
def test_linked_subexpression_3():
'''
Test a linked variable referring to a subexpression with indices
'''
G = NeuronGroup(2, '''dv/dt = 100*Hz : 1
I = clip(v, 0, inf) : 1''',
threshold='v>1', reset='v=0')
G.v = [0, .5]
G2 = NeuronGroup(10, '''I_l : 1 (linked) ''')
G2.I_l = linked_var(G.I, index=np.array([0, 1]).repeat(5))
mon1 = StateMonitor(G, 'I', record=True)
mon = StateMonitor(G2, 'I_l', record=True)
run(5*ms)
# Due to the linking, the first 5 and the second 5 recorded I vectors should
# refer to the
assert all((all(mon[i].I_l == mon1[0].I) for i in xrange(5)))
assert all((all(mon[i+5].I_l == mon1[1].I) for i in xrange(5)))
def test_linked_variable_scalar():
'''
Test linked variable from a size 1 group.
'''
G1 = NeuronGroup(1, 'dx/dt = -x / (10*ms) : 1')
G2 = NeuronGroup(10, '''dy/dt = (-y + x) / (20*ms) : 1
x : 1 (linked)''')
G1.x = 1
G2.y = np.linspace(0, 1, 10)
G2.x = linked_var(G1.x)
mon = StateMonitor(G2, 'y', record=True)
# We don't test anything for now, except that it runs without raising an
# error
run(10*ms)
# Make sure that printing the variable values works
assert len(str(G2.x)) > 0
assert len(repr(G2.x)) > 0
assert len(str(G2.x[:])) > 0
assert len(repr(G2.x[:])) > 0
def test_linked_subexpression_3():
'''
Test a linked variable referring to a subexpression with indices
'''
G = NeuronGroup(2, '''dv/dt = 100*Hz : 1
I = clip(v, 0, inf) : 1''',
threshold='v>1', reset='v=0')
G.v = [0, .5]
G2 = NeuronGroup(10, '''I_l : 1 (linked) ''')
G2.I_l = linked_var(G.I, index=np.array([0, 1]).repeat(5))
mon1 = StateMonitor(G, 'I', record=True)
mon = StateMonitor(G2, 'I_l', record=True)
run(5*ms)
# Due to the linking, the first 5 and the second 5 recorded I vectors should
# refer to the
assert all((all(mon[i].I_l == mon1[0].I) for i in xrange(5)))
assert all((all(mon[i+5].I_l == mon1[1].I) for i in xrange(5)))
def test_linked_subexpression_synapse():
'''
Test a complicated setup (not unlikely when using brian hears)
'''
G = NeuronGroup(2, 'dv/dt = 100*Hz : 1',
threshold='v>1', reset='v=0')
G.v = [0, .5]
G2 = NeuronGroup(10, '''I = clip(x, 0, inf) : 1
x : 1 (linked) ''')
# This will not be able to include references to `I` as `I_pre` etc., since
# the indirect indexing would have to change depending on the synapses
G2.x = linked_var(G.v, index=np.array([0, 1]).repeat(5))
S = Synapses(G2, G2, '')
S.connect('i==j')
assert 'I' not in S.variables
assert 'I_pre' not in S.variables
assert 'I_post' not in S.variables
assert 'x' not in S.variables
assert 'x_pre' not in S.variables
assert 'x_post' not in S.variables
